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Anthropocentric data analysis.
~
Lewis, Joshua M.
Anthropocentric data analysis.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
書名/作者:
Anthropocentric data analysis.
作者:
Lewis, Joshua M.
面頁冊數:
92 p.
附註:
Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3748.
Contained By:
Dissertation Abstracts International72-06B.
標題:
Psychology, Cognitive.
標題:
Computer Science.
ISBN:
9781124572284
摘要、提要註:
Machine learning techniques benefit science and industry primarily insofar as they enable data analysts to better understand their data, make valid conclusions, and gain insight into their domain of investigation. Studies in anthropocentric data analysis seek to understand how human judgment is applied in the data analysis process and to develop methods that provide explicit opportunities for human interaction and insight. We present three studies of human visual reasoning exploring the extent to which novice and expert subjects are able to judge the quality of and understand cluster analysis and dimensionality reduction stimuli. We then investigate whether humans are able to learn how cluster analysis algorithms function simply through interacting with them rapidly across diverse data sets. Finally we show how multicore processing on CPUs and GPUs can move human/algorithm interactions closer to real-time.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3449661
Anthropocentric data analysis.
Lewis, Joshua M.
Anthropocentric data analysis.
- 92 p.
Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3748.
Thesis (Ph.D.)--University of California, San Diego, 2011.
Machine learning techniques benefit science and industry primarily insofar as they enable data analysts to better understand their data, make valid conclusions, and gain insight into their domain of investigation. Studies in anthropocentric data analysis seek to understand how human judgment is applied in the data analysis process and to develop methods that provide explicit opportunities for human interaction and insight. We present three studies of human visual reasoning exploring the extent to which novice and expert subjects are able to judge the quality of and understand cluster analysis and dimensionality reduction stimuli. We then investigate whether humans are able to learn how cluster analysis algorithms function simply through interacting with them rapidly across diverse data sets. Finally we show how multicore processing on CPUs and GPUs can move human/algorithm interactions closer to real-time.
ISBN: 9781124572284Subjects--Topical Terms:
423116
Psychology, Cognitive.
Anthropocentric data analysis.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3449661
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